Missing Data: Consequences and Solutions (WK81)

Course ContentAlthough researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large your missing data problem is and how to deal with it depends on how much data is missing and why your data are missing. This two-day course provides you with tools how to evaluate and handle missing data in medical and epidemiological studies with different missing data rates.

Learning objectives, training objectivesThe participant is able to distinguish between different missing data mechanisms called missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR).

Target AudienceThe course is designed for everybody who wants to learn about missing data because missing data may be present in your own research you want to learn how to judge other articles or research grants.

QualificationsThe following concepts are assumed known by participants at the start of this course:
- Knowledge of basic statistical tests as t-tests and regression analyses.
- Knowledge of some basic SPSS commands.

NotesOn the first day of the course you will receive a package containing copies of all the lecture presentations and computer exercises, assignments, and feedback on these assignments.